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Project Plan

Project Plan

Project Plan

What features to take?

Important measures to check

pyrad$volume = as.numeric(pyrad$lesion1sizepath)
pyrad$volume_from_pyrad = as.numeric(pyrad$original_shape_MeshVolume)
pyrad$diameter = as.numeric(pyrad$original_shape_Maximum2DDiameterSlice)

how to define radITH

pyrad$radITH = rowMeans(pyrad[,features_of_interest], na.rm = T)
Q = 3


pyrad$volume_group = gtools::quantcut(pyrad$volume, q=Q, na.rm=TRUE)
pyrad$diameter_group = gtools::quantcut(pyrad$diameter, q=Q, na.rm=TRUE)
pyrad$radITH_group = gtools::quantcut(pyrad$radITH, q=Q, na.rm=TRUE)

Expected correlations

Mutations

Let’s group DRIVER mutations by Sanchez Vega def

Let’s test Sanchez Vega Muts vs radITH groups (q =3)

## [1] "Adeno fisher test results"
## [1] "nrf2"
## 
##  Fisher's Exact Test for Count Data
## 
## data:  table(tmp$radITH_group, tmp[, col])
## p-value = 0.02679
## alternative hypothesis: two.sided
## 
## [1] "pi3k"
## 
##  Fisher's Exact Test for Count Data
## 
## data:  table(tmp$radITH_group, tmp[, col])
## p-value = 0.003382
## alternative hypothesis: two.sided
## 
## [1] "cell_cycle"
## 
##  Fisher's Exact Test for Count Data
## 
## data:  table(tmp$radITH_group, tmp[, col])
## p-value = 0.0003902
## alternative hypothesis: two.sided
## 
## [1] "wnt"
## 
##  Fisher's Exact Test for Count Data
## 
## data:  table(tmp$radITH_group, tmp[, col])
## p-value = 0.04017
## alternative hypothesis: two.sided
## [1] "Squamous fisher test results"
## [1] "rtk_kras"
## 
##  Fisher's Exact Test for Count Data
## 
## data:  table(tmp$radITH_group, tmp[, col])
## p-value = 0.01748
## alternative hypothesis: two.sided
## 
## [1] "pi3k"
## 
##  Fisher's Exact Test for Count Data
## 
## data:  table(tmp$radITH_group, tmp[, col])
## p-value = 0.007167
## alternative hypothesis: two.sided

Does Volume or diameter predict biology?

## [1] "Adeno fisher test results"
## [1] "Squamous fisher test results"
## [1] "hippo"
## 
##  Fisher's Exact Test for Count Data
## 
## data:  table(tmp$volume_group, tmp[, col])
## p-value = 0.01164
## alternative hypothesis: two.sided

How does radITH associate with survival?

How does volume (diameter) associate to survival?

Can we overlap radITH and Volume groups and check survival?

In order to increase group sizes, all measures will be split by median (Q=2)

Coxph Model

Hallmarks all samples

Hallmarks Adeno

Hallmarks Squamous

Hallmark expression-radITH Correlation in Large vs Small tumors (all samples)

Let’s split by Size and Pathology and repeat

Picking genes for Gene Expression Analysis

## Number of Genes after cutoff:  10332

Gene Expression Analysis without volume

Gene Expression Analysis by volume group and cancer type